diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..f56f150 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -19,19 +19,24 @@
 def green_mean():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)"""
     # TODO: 你的程式碼
-    pass
+    arr = np.array([10, 20, 30, 40, 50])
+    return(np.mean(arr))
 
 
 def green_double():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray"""
     # TODO: 你的程式碼
-    pass
+    arr = np.array([10, 20, 30, 40, 50])
+    return(arr*2)
 
 
 def green_filter():
     """建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)"""
     # TODO: 你的程式碼
-    pass
+    arr = np.array([10, 20, 30, 40, 50])
+    mask = arr > 25
+    return( arr[mask])
+
 
 
 # ============================================================
@@ -42,7 +47,12 @@ def green_filter():
 def yellow_expensive_count(prices):
     """回傳單價 > 1000 的商品數量 (int)"""
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce/products.csv'
+    prices = np.genfromtxt(DATA, delimiter=',', skip_header=1, usecols=3)  
+
+    mask1 = prices > 1000
+    result = mask1.sum()
+    return result
 
 
 def yellow_top3_stock_indices(stocks):
@@ -51,7 +61,11 @@ def yellow_top3_stock_indices(stocks):
     提示：np.argsort
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce/products.csv'
+    stocks = np.genfromtxt(DATA, delimiter=',', skip_header=1, usecols=4)
+    
+    return np.argsort(stocks)[-3:]
+
 
 
 def yellow_restock_cost(prices, stocks):
@@ -60,7 +74,13 @@ def yellow_restock_cost(prices, stocks):
     提示：布林遮罩 + .sum()
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce/products.csv'
+    prices = np.genfromtxt(DATA, delimiter=',', skip_header=1, usecols=3) 
+    stocks = np.genfromtxt(DATA, delimiter=',', skip_header=1, usecols=4)
+    low_prices = prices < 500
+    return(sum(prices[low_prices])*50)
+
+                        
 
 
 # ============================================================
@@ -77,4 +97,9 @@ def red_double11_prices(prices, stocks):
     提示：np.where 可以巢狀使用
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce/products.csv'
+    prices = np.genfromtxt(DATA, delimiter=',', skip_header=1, usecols=3) 
+    stocks = np.genfromtxt(DATA, delimiter=',', skip_header=1, usecols=4)
+
+    result1 = np.where( stocks>=100,prices*0.7,np.where((stocks>19) &(stocks<100),prices*0.9,prices))                 
+    return(result1)
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..96af6a5 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -20,7 +20,8 @@ def green_read_csv():
     提示：pd.read_csv()
     """
     # TODO: 你的程式碼
-    pass
+    DataFrame =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    return DataFrame
 
 
 def green_shape(df):
@@ -29,7 +30,8 @@ def green_shape(df):
     提示：df.shape
     """
     # TODO: 你的程式碼
-    pass
+    df =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    return df.shape
 
 
 def green_dtypes(df):
@@ -38,7 +40,8 @@ def green_dtypes(df):
     提示：df.dtypes
     """
     # TODO: 你的程式碼
-    pass
+    df =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    return df.dtypes
 
 
 # ============================================================
@@ -52,7 +55,10 @@ def yellow_clean_columns(df):
     提示：df.columns.str.strip().str.lower()
     """
     # TODO: 你的程式碼
-    pass
+    df =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    df.columns = df.columns.str.strip().str.lower()
+    return df
+
 
 
 def yellow_clean_amount(df):
@@ -63,7 +69,18 @@ def yellow_clean_amount(df):
     提示：.str.replace() + .astype(float)
     """
     # TODO: 你的程式碼
-    pass
+    df =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    df['amount'] = (
+    df['amount']
+    .astype(str)
+    .str.replace('$', '', regex=False)
+    .str.replace(',', '', regex=False)
+    .astype(float)
+    )
+    return df
+
+
+
 
 
 def yellow_drop_duplicates(df):
@@ -72,7 +89,11 @@ def yellow_drop_duplicates(df):
     提示：df.drop_duplicates()
     """
     # TODO: 你的程式碼
-    pass
+    df =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    df = df.drop_duplicates()
+    return df
+
+
 
 
 # ============================================================
@@ -93,4 +114,29 @@ def red_clean_orders(path):
     提示：pd.to_datetime(errors='coerce')
     """
     # TODO: 你的程式碼
-    pass
+    df =pd.read_csv("datasets/ecommerce/orders_raw.csv")
+    df.columns = df.columns.str.strip().str.lower()
+    df['amount'] = (
+    df['amount']
+    .astype(str)
+    .str.replace('$', '', regex=False)
+    .str.replace(',', '', regex=False)
+    .astype(float)
+    )
+    df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+    df = df.dropna(subset=['order_date'])
+    df = df.dropna(subset=['amount'])
+    df = df.drop_duplicates()
+    return df
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..3348a98 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -24,19 +24,49 @@ def green_load_and_merge():
     提示：pd.merge(how='left')
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce' 
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+    df = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    return df
+
+
+
 
 
 def green_row_count(df):
     """回傳 DataFrame 的列數 (int)"""
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce' 
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+    df = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    return int(df.shape[0])
 
 
 def green_column_list(df):
     """回傳 DataFrame 的所有欄位名稱 (list)"""
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce' 
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+    df = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    return list(df.columns)
 
 
 # ============================================================
@@ -50,7 +80,19 @@ def yellow_top_category(df):
     提示：groupby('category')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce' 
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+    df = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    high_category = df.groupby('category')['amount'].sum()
+
+    return(high_category.index[0])
+
 
 
 def yellow_gold_vip_stats(df):
@@ -60,7 +102,20 @@ def yellow_gold_vip_stats(df):
     提示：df[df['vip_level'] == 'Gold']
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce' 
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+    df = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    vip = df[df['vip_level'] == 'Gold']
+    viporder = int(vip.shape[0])
+    vipamount =vip['amount'].sum()
+
+    return viporder,vipamount 
 
 
 def yellow_region_avg_amount(df):
@@ -70,7 +125,17 @@ def yellow_region_avg_amount(df):
     提示：groupby('region')['amount'].mean()
     """
     # TODO: 你的程式碼
-    pass
+    DATA = 'datasets/ecommerce' 
+    orders    = pd.read_csv(f'{DATA}/orders_clean.csv', parse_dates=['order_date'])
+    customers = pd.read_csv(f'{DATA}/customers.csv')
+    products  = pd.read_csv(f'{DATA}/products.csv')
+    df = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    regionamount = df.groupby('region')['amount'].mean()
+    return regionamount
 
 
 # ============================================================
@@ -94,4 +159,16 @@ def red_rfm_top5(df):
     提示：groupby('customer_id').agg(...)
     """
     # TODO: 你的程式碼
-    pass
+    multi = df.groupby("customer_id").agg(
+    customer_name=('customer_name','first'), 
+    R=('order_date', 'max' ), 
+    F=('order_id', 'count'),
+    M=('amount', 'sum')
+    ).reset_index()
+    multi_sorted = multi.sort_values('M', ascending=False).head()
+
+    return multi_sorted
+
+
+
+
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..eabc8f9 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -27,7 +27,15 @@ def green_avg_by_month():
     提示：df['order_date'].dt.month
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"]) 
+     
+    df['month']    = df['order_date'].dt.month
+    ts = df.set_index('order_date').sort_index()
+    monthly = ts['amount'].resample('ME').mean()
+    monthly.index = monthly.index.month
+
+    return monthly
 
 
 def green_top3_dates():
@@ -37,7 +45,12 @@ def green_top3_dates():
     提示：value_counts().head(3)
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    
+    mostorderdate = df['order_date'].value_counts().head(3)
+
+    return mostorderdate
 
 
 def green_date_range():
@@ -46,7 +59,11 @@ def green_date_range():
     格式為 pandas Timestamp
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    Timestamp = df['order_date'].min() ,df['order_date'].max()
+
+    return Timestamp
 
 
 # ============================================================
@@ -60,7 +77,14 @@ def yellow_monthly_revenue():
     提示：set_index('order_date').resample('ME')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    ts = df.set_index('order_date').sort_index()
+    monthly_revenue = ts['amount'].resample('ME').sum()
+
+    return  monthly_revenue
+    
+
 
 
 def yellow_rolling_avg(monthly_revenue):
@@ -71,7 +95,17 @@ def yellow_rolling_avg(monthly_revenue):
     提示：.rolling(window=3).mean()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    ts = df.set_index('order_date').sort_index()
+    monthly_revenue = ts['amount'].resample('ME').sum()
+    ma3   = monthly_revenue.rolling(window=3).mean()
+
+
+    return ma3 
+
+
+
 
 
 def yellow_category_median(df):
@@ -81,7 +115,11 @@ def yellow_category_median(df):
     提示：groupby + median + sort_values
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    categorymidian =df.groupby('category')['amount'].median().sort_values(ascending=False)
+
+    return categorymidian
 
 
 # ============================================================
@@ -101,4 +139,26 @@ def red_monthly_report():
     提示：resample + agg + pct_change
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    df['year']     = df['order_date'].dt.year
+    df['month']    = df['order_date'].dt.month
+    df['weekday']  = df['order_date'].dt.day_name()
+    df['year_mon'] = df['order_date'].dt.to_period('M')
+
+    Monthly_Report =df.groupby('month').agg(
+    order_count = ('order_id','count'),
+    revenue =('amount','sum'),
+    active_customers = ('customer_name','nunique'),
+)
+    Monthly_Report['avg_order_value'] = Monthly_Report ['revenue']/ Monthly_Report['order_count']
+    Monthly_Report['revenue_growth']= Monthly_Report ['revenue'].pct_change()
+
+
+    return(Monthly_Report)
+
+
+
+
+    
+
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..0799fe3 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -29,7 +29,18 @@ def green_bar_category():
     提示：sns.countplot 或 value_counts().plot.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                       parse_dates=["order_date"])
+    
+    category_counts = df.groupby('category')['order_id'].count().reset_index()
+
+    fig, ax = plt.subplots()
+
+    sns.barplot(data=category_counts, x='category', y='order_id', ax=ax, color='green')
+    
+    ax.set_title('Order Count by Category')
+
+    return  fig
 
 
 def green_hist_amount():
@@ -39,7 +50,12 @@ def green_hist_amount():
     提示：sns.histplot(bins=20) 或 plt.hist()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                       parse_dates=["order_date"])
+    fig, ax = plt.subplots()
+    sns.histplot(data=df['amount'],bins=20,ax=ax)
+
+    return fig
 
 
 def green_set_labels():
@@ -51,7 +67,18 @@ def green_set_labels():
     回傳 matplotlib Figure 物件
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                       parse_dates=["order_date"])
+    
+    category_counts = df.groupby('category')['order_id'].count().reset_index()
+
+    fig, ax = plt.subplots()
+
+    sns.barplot(data=category_counts, x='category', y='order_id', ax=ax, color='green')
+    
+    ax.set_title('Order Count by Category')
+
+    return  fig
 
 
 # ============================================================
@@ -68,7 +95,21 @@ def yellow_line_region_trend():
     提示：分別 groupby 再 plot，或用 sns.lineplot(hue='region')
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                       parse_dates=["order_date"])
+    df['month']    = df['order_date'].dt.month
+    monthly_North = df.query("region == 'North'").groupby('month')['amount'].sum().reset_index()
+    monthly_South = df.query("region == 'South'").groupby('month')['amount'].sum().reset_index()
+
+    fig = plt.figure(figsize=(10, 4))
+    sns.lineplot(data=monthly_North, x='month', y='amount', marker='o', linewidth=2)
+    sns.lineplot(data=monthly_South, x='month', y='amount', marker='s', linewidth=2)
+    plt.title('Line Region Trend', fontsize=14, fontweight='bold')
+    plt.xlabel('Month')
+    plt.ylabel('Revenue (NT$)')
+    plt.ylabel('Revenue (NT$)')
+
+    return  fig
 
 
 def yellow_box_vip():
@@ -78,8 +119,19 @@ def yellow_box_vip():
     提示：sns.boxplot(x='vip_level', y='amount', data=df)
     """
     # TODO: 你的程式碼
-    pass
-
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                parse_dates=["order_date"])
+    
+    fig = plt.figure(figsize=(9, 4))
+    sns.boxplot(data=df, x='vip_level', y='amount', palette='Set2', hue='category', legend=False)
+    plt.title('box_vip', fontweight='bold')
+    plt.xlabel('vip_level')
+    plt.ylabel('amount')
+    plt.xticks(rotation=15)
+    plt.tight_layout()
+    plt.show()
+
+    return  fig
 
 def yellow_scatter_price_amount():
     """
@@ -88,7 +140,20 @@ def yellow_scatter_price_amount():
     提示：plt.scatter() 或 sns.scatterplot()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                parse_dates=["order_date"])
+    
+    fig = plt.figure(figsize=(9, 5))
+    sns.scatterplot(data=df, x='unit_price', y='amount',
+                hue='category', alpha=0.6, s=60)
+    plt.title('scatter price amount', fontweight='bold')
+    plt.xlabel('Unit Price')
+    plt.ylabel('Amount')
+    plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left')
+    plt.tight_layout()
+    plt.show()
+
+    return  fig 
 
 
 # ============================================================
@@ -107,4 +172,45 @@ def red_category_dashboard(category="Electronics"):
     提示：fig, axes = plt.subplots(2, 2, figsize=(14, 10))
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                parse_dates=["order_date"])
+    df['month']    = df['order_date'].dt.month
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+    
+    monthly_Electronics = df.query("category == 'Electronics'").groupby('month')['amount'].sum().reset_index()
+
+    sns.lineplot(data=monthly_Electronics, x='month', y='amount', marker='o', linewidth=2,color='brown', ax=axes[0, 0])
+    plt.title('category dashboard', fontsize=14, fontweight='bold')
+    plt.xlabel('Month')
+    plt.ylabel('Revenue (NT$)')
+
+
+    monthly_Electronics1 = df.query("category == 'Electronics'").groupby('region')['amount'].sum().sort_values(ascending=False).reset_index()
+
+    sns.barplot(data=monthly_Electronics1, x='region', y='amount', palette='viridis', hue='region', legend=False, ax=axes[0, 1])
+    plt.title('Revenue by Electronics', fontweight='bold')
+    plt.xlabel('Region')
+    plt.ylabel('Revenue (NT$)')
+
+
+    monthly_Electronics2 = df.query("category == 'Electronics'").groupby('product_name')['amount'].sum().sort_values(ascending=False).reset_index().head()
+
+    sns.barplot(data=monthly_Electronics2, x='amount', y='product_name', color= 'green',ax=axes[1, 0])
+    plt.title('Revenue by Electronics', fontweight='bold')
+    plt.xlabel('Region')
+    plt.ylabel('Revenue (NT$)')    
+
+
+    monthly_Electronics3 = df.query("category == 'Electronics'").groupby('product_name')['amount'].sum().sort_values(ascending=False).reset_index()
+
+    sns.histplot(data=monthly_Electronics3,x='amount',y='product_name',bins=20, color='pink', ax=axes[1, 1])
+
+    plt.tight_layout()
+
+
+    return fig
+
+
+
+
+
diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py
index 0e2c32a..658ef43 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -26,7 +26,15 @@ def green_plotly_bar():
     提示：px.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                parse_dates=["order_date"])
+    plotly_bar = df.groupby('category', as_index=False)['amount'].sum().sort_values('amount', ascending=False)
+    fig = px.bar(plotly_bar, x='category', y='amount', text='amount',color='category', title='Plotly Bar')
+    fig.update_traces(texttemplate='%{text:,.0f}', textposition='outside')
+    fig.update_layout(height=480, showlegend=False)
+
+    return fig
+    
 
 
 def green_plotly_line():
@@ -37,8 +45,16 @@ def green_plotly_line():
     提示：先 groupby 月份算總營收，再 px.line()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                parse_dates=["order_date"])
+    df['month']    = df['order_date'].dt.month
+    df['month']    = df['order_date'].dt.month
+    monthly1 = df.groupby('month')['amount'].sum().reset_index()
+    fig = px.line(monthly1, x='month', y='amount', markers=True,
+              title='plotly line')
+    fig.update_layout(height=400)
 
+    return fig
 
 def green_plotly_pie():
     """
@@ -48,8 +64,14 @@ def green_plotly_pie():
     提示：px.pie()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                parse_dates=["order_date"])
+    vip_rev = df.groupby('vip_level', as_index=False)['amount'].sum()
+    fig = px.pie(vip_rev, names='vip_level', values='amount',
+             title='VIP Level Share', hole=0.4)  
+    fig.update_layout(height=400)
 
+    return fig
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
@@ -63,7 +85,32 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path):
     回傳：合併後的 DataFrame
     """
     # TODO: 你的程式碼
-    pass
+    def load_and_clean_orders(path):
+        df = pd.read_csv(path)
+        df.columns = df.columns.str.strip().str.lower()
+        df['amount'] = (
+        df['amount'].astype(str)
+        .str.replace('$','', regex=False)
+        .str.replace(',','', regex=False)
+        .astype(float)
+        )
+        df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+        df = df.dropna(subset=['order_date'])
+        df['qty'] = df['qty'].fillna(df['qty'].median())
+        df = df.drop_duplicates()
+
+        return df
+
+    orders = load_and_clean_orders('datasets/ecommerce/orders_raw.csv')
+    customers = pd.read_csv('datasets/ecommerce/customers.csv')
+    products  = pd.read_csv('datasets/ecommerce/products.csv')
+    full = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+
+    return full
 
 
 def yellow_kpi_summary(df):
@@ -77,7 +124,20 @@ def yellow_kpi_summary(df):
     }
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv("datasets/ecommerce/orders_enriched.csv",
+                     parse_dates=["order_date"])
+    total_revenue = df['amount'].sum()
+    order_count = df['order_id'].count()
+    active_customers =df['customer_name'].nunique()
+    avg_order_value = total_revenue / order_count 
+
+    kpi_summary = {
+        "total_revenue": total_revenue ,      
+        "order_count": order_count,          
+        "active_customers": active_customers,      
+        "avg_order_value": avg_order_value ,    
+    }
+    return kpi_summary 
 
 
 def yellow_plotly_scatter(df):
@@ -91,8 +151,12 @@ def yellow_plotly_scatter(df):
     提示：px.scatter(hover_data=['product_name'])
     """
     # TODO: 你的程式碼
-    pass
+    fig = px.scatter(df, x='unit_price', y='amount',
+                 color='category', hover_data=['product_name', 'customer_name'],
+                 title='Unit Price vs Order Amount')
+    fig.update_layout(height=450)
 
+    return  fig 
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -115,4 +179,72 @@ def red_dashboard():
     提示：from plotly.subplots import make_subplots
     """
     # TODO: 你的程式碼
-    pass
+    def load_and_clean_orders(path):
+        df = pd.read_csv(path)
+        df.columns = df.columns.str.strip().str.lower()
+        df['amount'] = (
+        df['amount'].astype(str)
+        .str.replace('$','', regex=False)
+        .str.replace(',','', regex=False)
+        .astype(float)
+        )
+        df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+        df = df.dropna(subset=['order_date'])
+        df['qty'] = df['qty'].fillna(df['qty'].median())
+        df = df.drop_duplicates()
+
+        return df
+
+    orders = load_and_clean_orders('datasets/ecommerce/orders_raw.csv')
+    customers = pd.read_csv('datasets/ecommerce/customers.csv')
+    products  = pd.read_csv('datasets/ecommerce/products.csv')
+    enriched = (
+    orders
+    .merge(customers, on='customer_id', how='left')
+    .merge(products,  on='product_id',  how='left')
+    )
+    enriched['month']    = enriched['order_date'].dt.month
+
+    monthly     = enriched.groupby('month', as_index=False)['amount'].sum()
+    top_prod    = (enriched.groupby('product_name', as_index=False)['amount']
+               .sum().sort_values('amount', ascending=False).head(10))
+    region_rev  = enriched.groupby('region', as_index=False)['amount'].sum()
+    cat_rev     = enriched.groupby('category', as_index=False)          ['amount'].sum()
+
+# 2x2 subplot
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=('Monthly Revenue Trend',
+                    'Top 10 Products',
+                    'Revenue by Region',
+                    'Category Share'),
+    specs=[[{'type':'xy'}, {'type':'xy'}],
+           [{'type':'xy'}, {'type':'domain'}]],
+    )
+
+    fig.add_trace(go.Scatter(x=monthly['month'], y=monthly['amount'],
+                         mode='lines+markers', name='Monthly'), row=1, col=1)
+    fig.add_trace(go.Bar(x=top_prod['product_name'], y=top_prod['amount'],
+                     name='Top Products'), row=1, col=2)
+    fig.add_trace(go.Bar(x=region_rev['region'], y=region_rev['amount'],
+                     name='Region'), row=2, col=1)
+    fig.add_trace(go.Pie(labels=cat_rev['category'], values=cat_rev['amount'],
+                     name='Category', hole=0.4), row=2, col=2)
+
+    fig.update_layout(
+    title_text='<b>E-Commerce Sales Dashboard — 2025</b>',
+    height=750, showlegend=False,
+    )
+    fig.update_xaxes(tickangle=45, row=1, col=2)
+
+
+    return fig
+
+
+
+
+
+
+
+
+